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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is Stochastic Gradient Descent (SGD)?
💡 Hint: Think about how it differs from batch gradient descent.
Question 2
Easy
Why is a learning rate important in SGD?
💡 Hint: Consider what happens if the learning rate is too high or too low.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does SGD stand for?
- Standardized Gradient Decent
- Stochastic Gradient Descent
- Static Gradient Decision
💡 Hint: Think about how it utilizes data differently from batch methods.
Question 2
True or False: SGD calculates the gradient based on the entire dataset.
- True
- False
💡 Hint: Reflect on the definition of stochastic as it relates to the term 'entire dataset'.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Analyze SGD's performance on a dataset with varying levels of noise. How would you expect SGD’s oscillations to vary in this scenario?
💡 Hint: Consider how noise might influence the gradient calculations.
Question 2
Given a dataset of 100,000 samples, design a mini-batch size for SGD that balances training time and convergence stability.
💡 Hint: Think about how batch sizes affect the update frequency.
Challenge and get performance evaluation